require vs library
# load libraries
library(raster)
## Loading required package: sp
library(rgdal)
## rgdal: version: 1.1-10, (SVN revision 622)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 1.11.4, released 2016/01/25
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.3/Resources/library/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.3/Resources/library/rgdal/proj
## Linking to sp version: 1.2-3
chm <- raster("../NEONdata/D17-California/TEAK/2013/lidar/TEAK_lidarCHM.tif")
plot(chm,
main="this plots using the raster package")
image(chm,
main="these are just pixels and will stretch the space")
hist(chm)
## Warning in .hist1(x, maxpixels = maxpixels, main = main, plot = plot, ...):
## 32% of the raster cells were used. 100000 values used.
chm[chm==0] <- NA
hist(chm,
xlab="Tree Height (m)")
aspect <- raster("../NEONdata/D17-California/TEAK/2013/lidar/TEAK_lidarAspect.tif")
plot(aspect,
main="Aspect data for Teakettle Field Site")
# Create matrix
class.m <- c(0, 45, 1,
45, 135, NA,
135, 225, 2,
225, 315, NA,
315, 360, 1)
rcl.m <- matrix(class.m,
ncol = 3,
byrow=TRUE)
rcl.m
## [,1] [,2] [,3]
## [1,] 0 45 1
## [2,] 45 135 NA
## [3,] 135 225 2
## [4,] 225 315 NA
## [5,] 315 360 1
asp.ns <- reclassify(aspect,
rcl.m)
plot(asp.ns,
main="North and South Facing Slopes")
writeRaster(asp.ns,
file="../outputs/TEAK/Teak_nsAspect2.tif",
options="COMPRESS=LZW",
NAflag = -9999)
asp.ns
## class : RasterLayer
## dimensions : 577, 543, 313311 (nrow, ncol, ncell)
## resolution : 1, 1 (x, y)
## extent : 325963, 326506, 4102905, 4103482 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
## data source : in memory
## names : layer
## values : 0, 2 (min, max)
ndvi <- raster("../NEONdata/D17-California/TEAK/2013/spectrometer/veg_index/TEAK_NDVI.tif")
plot(ndvi,
main="NDVI for teakettle field site")
# mask data
nFacing.ndvi <- mask(ndvi,
asp.ns)
plot(nFacing.ndvi)